Login / Signup

Assessment of gabapentin misuse using prescription drug monitoring program data.

Yifan ZhangAlexandria R CastracaneErin L Winstanley
Published in: Substance abuse (2021)
Background: Gabapentin is an anticonvulsant medication with potential misuse reported in case reports and population studies, highlighting the need to reexamine its abuse liability. The purpose of this study was to describe gabapentin dispensing patterns and assess potential misuse. Methods: We used data from Ohio's Prescription Drug Monitoring Program (PDMP) from December 1, 2016 to March 31, 2017 and restricted the population to adults who filled at least one gabapentin prescription (N = 379,372). Gabapentin dispensing patterns are described and multiple strategies were used to assess potential misuse, including Lorenz-1 curve analysis. Supratherapeutic dosing, number of prescribers and number of pharmacies used were compared for individuals who were co-dispensed medications for opioid use disorder (MOUD) and those who were not. Results: More than one million gabapentin prescriptions were dispensed during the 4-month period, with a mean dose of 1103.8 mg. While few individuals received supratherapeutic dosing, exceptionally high doses were observed. Half of the individuals (50.9%) were co-dispensed gabapentin and opioids. The Lorenz-1 value for gabapentin (5.5%) did not exceed the threshold for misuse potential. Individuals co-dispensed MOUD were more likely to have supratherapeutic dosing; however, they had a lower Lorenz-1 value compared to individuals not co-dispensed MOUD. Conclusions: Among Ohio residents dispensed gabapentin, there was no evidence of misuse using PDMP data based on the Lorenz-1 value, yet supratherapeutic dosing of gabapentin was observed and was associated with OUD. New strategies may be needed to identify the non-medical use of gabapentin.
Keyphrases
  • neuropathic pain
  • chronic pain
  • spinal cord
  • spinal cord injury
  • healthcare
  • electronic health record
  • human health
  • emergency department
  • risk assessment
  • quality improvement
  • case report
  • machine learning
  • climate change